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#General #Development #Mobile Development #Cloud & DevOps #Cybersecurity & Privacy #AI & Data Science #Hardware & Gadgets #Design & UXGetting The Most From The LangChain Ecosystem
Learn how to use the LangChain ecosystem to build, test, deploy, monitor, and visualize complex agentic workflows.
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From Data Scientist IC to Manager: One Year In
Three pillars that shaped my first year in data science management-prioritization, empowerment, and recognition The post From Data Scientist IC to Manager: One YearIn appeared first on Towards Data Science.
Introducing Server-Sent Events in Python
A simpler path to coding real-time web applications. The post Introducing Server-Sent Events inPython appeared first on Towards Data Science.
Master the Future with Applied Data Science Top-Ranked, Affordable, Online Master’s Degree Program
No. 4 in Fortune’s 2024 Best Online Master’s in Data Science Programs and No. 10 in TechGuide’s 2025 Most Affordable Online Master’s in Data Science Degree Programs.
On Adding a Start Value to a Waterfall Chart in Power BI
A waterfall chart can be a powerful tool for conveying information. But it has some limitations. The post On Adding a Start Value to a Waterfall Chart in Power BI appeared first on Towards Data Science.
Hands-On with Agents SDK: Multi-Agent Collaboration
Explore the handoff and agents-as-tools patterns, their use cases, and how to customize them using OpenAI Agents SDK and Streamlit. The post Hands-On with Agents SDK: Multi-Agent Collaboration appeared first on Towards Data Science.
Rethinking how we measure AI intelligence
Game Arena is a new, open-source platform for rigorous evaluation of AI models. It allows for head-to-head comparison of frontier systems in environments with clear winning conditions.
Building a Decoder-Only Transformer Model Like Llama-2 and Llama-3
This post is divided into five parts; they are: • From a Full Transformer to a Decoder-Only Model • Building a Decoder-Only Model • Data Preparation for Self-Supervised Learning • Training the Model • Extensions The transformer model originated as a sequence-to-sequence (seq2seq) model that converts an input sequence into a context vector, which is then used to generate a new sequence.
Building a Decoder-Only Transformer Model for Text Generation
This post is divided into five parts; they are: • From a Full Transformer to a Decoder-Only Model • Building a Decoder-Only Model • Data Preparation for Self-Supervised Learning • Training the Model • Extensions The transformer model originated as a sequence-to-sequence (seq2seq) model that converts an input sequence into a context vector, which is then used to generate a new sequence.
Does the Code Work or Not?
A common misconception about the working state of code in data, AI or software engineering fields. The post Does the Code Work orNot? appeared first on Towards Data Science.
Exploring NotebookLM Alternatives
Looking for tools like NotebookLM? Discover alternatives that help you organize, summarize, and interact with your documents.
10 Python Libraries Every MLOps Engineer Should Know
Learn about 10 essential Python libraries that support core MLOps tasks like versioning, deployment, and monitoring.
5 Routine Tasks That ChatGPT Can Handle for Data Scientists
A practical walkthrough of how ChatGPT handles cleaning, exploration, visualization, modeling and more.
Building a Transformer Model for Language Translation
This post is divided into six parts; they are: • Why Transformer is Better than Seq2Seq • Data Preparation and Tokenization • Design of a Transformer Model • Building the Transformer Model • Causal Mask and Padding Mask • Training and Evaluation Traditional seq2seq models with recurrent neural networks have two main limitations: • Sequential processing prevents parallelization • Limited ability to capture long-term...
Mastering NLP with spaCy – Part 2
POS tagging, dependency parser and named entity recognition. The post Mastering NLP with spaCy – Part 2 appeared first on Towards Data Science.
How Computers “See” Molecules
Generative Molecular Design (Part 1): common molecular representations in data science. The post How Computers “See” Molecules appeared first on Towards Data Science.
“I think of analysts as data wizards who help their product teams solve problems”
Mariya Mansurova explains how hands-on learning, agentic AI, and engineering habits shape her writing and work. The post “I think of analysts as data wizards who help their product teams solve problems” appeared first on Towards Data Science.
Debugging and Tracing LLMs Like a Pro
Debugging LLMs is important because their workflows are complex and involve multiple parts like chains, prompts, APIs, tools, retrievers, and more.
When Models Stop Listening: How Feature Collapse Quietly Erodes Machine Learning Systems
Models don't just fail with noise; they fail in silence, by narrowing their attention to the point of fragility. The post When Models Stop Listening: How Feature Collapse Quietly Erodes Machine Learning Systems appeared first on Towards Data Science.
10 Surprising Things You Can Do with Python’s time module
This tutorial explores ten practical and surprising applications of the Python time module.
Try Deep Think in the Gemini app
Deep Think utilizes extended, parallel thinking and novel reinforcement learning techniques for significantly improved problem-solving.
Best Web Scraping Companies in 2025
Whether you’re looking for simple point-and-click solutions or hardcore APIs for scraping the entire web, this list offers something for everyone.
How to Diagnose Why Your Regression Model Fails
In regression models , failure occurs when the model produces inaccurate predictions — that is, when error metrics like MAE or RMSE are high — or when the model, once deployed, fails to generalize well to new data that differs from the examples it was trained or tested on.
FastSAM for Image Segmentation Tasks — Explained Simply
Image segmentation is a popular task in computer vision, with the goal of partitioning an input image into multiple regions, where each region represents a separate object. Several classic approaches from the past involved taking a model backbone (e.g., U-Net) and fine-tuning it on specialized datasets. While fine-tuning works well, the emergence of GPT-2 and […] The post FastSAM for Image Segmentation Tasks —...
How to Benchmark LLMs – ARC AGI 3
Learn how to LLMs are benchmarked, and try out the newly released ARC AGI 3 The post How to Benchmark LLMs – ARC AGI 3 appeared first on Towards Data Science.
LLMs and Mental Health
Are LLMs good or bad for our mental health? It’s more complicated than that. The post LLMs and Mental Health appeared first on Towards Data Science.
The ONLY Data Science Roadmap You Need to Get a Job
Are you looking to become a data scientist and don’t know where to start? In this article, I want to provide you with a straightforward, no-nonsense learning roadmap that you can follow to break into the industry. By the end, you’ll finally have a clear understanding of what is required and the best resources to […] The post The ONLY Data Science Roadmap You Need to Get aJob appeared first on Towards Data Science.
Top 7 Continuous Integration and Continuous Delivery Tools for 2025
Discover the leading CI/CD tools that are transforming how teams automate integration and deployment.
Best Proxy Providers in 2025
Discover the best proxy provider that fits your workflow, goals, and budget.
The Misconception of Retraining: Why Model Refresh Isn’t Always the Fix
Retraining is easy; knowing when not to is the real challenge. In machine learning, performance drops are rarely about stale weights; they’re about misunderstood signals. The post The Misconception of Retraining: Why Model Refresh Isn’t Always the Fix appeared first on Towards Data Science.
Confusion Matrix Made Simple: Accuracy, Precision, Recall & F1-Score
How to evaluate classification models and understand which metric matters the most. The post Confusion Matrix Made Simple: Accuracy, Precision, Recall & F1-Score appeared first on Towards Data Science.
What Is Data Literacy in 2025? It’s Not What You Think
In today’s fast-paced, distraction-heavy world, data literacy isn’t just about understanding charts or analyzing numbers—it’s about context, clarity, and human connection. With attention spans shrinking and AI-generated insights flooding our screens, even highly skilled professionals can behave like data novices. The real challenge isn’t technical know-how, but guiding overwhelmed audiences through stories that are...
Automated Testing: A Software Engineering Concept Data Scientists Must Know To Succeed
Why you should read this article Most data scientists whip up a Jupyter Notebook, play around in some cells, and then maintain entire data processing and model training pipelines in the same notebook. The code is tested once when the notebook was first written, and then it is neglected for some undetermined amount of time […] The post Automated Testing: A Software Engineering Concept Data Scientists Must Know To...
AlphaEarth Foundations helps map our planet in unprecedented detail
New AI model integrates petabytes of Earth observation data to generate a unified data representation that revolutionizes global mapping and monitoring
Implementing Advanced Feature Scaling Techniques in Python Step-by-Step
In this article, you will learn: • Why standard scaling methods are sometimes insufficient and when to use advanced techniques.